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All Journal Venn: Journal of Sustainable Innovation on Education, Mathematics and Natural Sciences Teknik: Jurnal Teknik Mesin, Elektro dan Ilmu Komputer
Muhammad Hidayatullah
Universitas Sulawesi Barat
Education Environmental Science Mathematics Physics Other

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Perbandingan Analisis Sentimen Ulasan Produk pada Platform E-Commerce Menggunakan Algoritma Naïve Bayes dan Random Forest Afif Budi Andy B; Kusnaeni Kusnaeni; Irwan Usman; Muhammad Hidayatullah; Muh. Rifandi
Venn: Journal of Sustainable Innovation on Education, Mathematics and Natural Sciences Vol. 5 No. 3 (2026): Riset Matematika dan Pendidikan Matematika
Publisher : Pusat Studi Bahasa dan Publikasi Ilmiah

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.53696/venn.v5i3.458

Abstract

Sentiment analysis has become increasingly important in e-commerce because product reviews influence consumer purchasing decisions and provide feedback for sellers to evaluate product quality and improve services. The large number of online reviews on e-commerce platforms makes manual analysis inefficient and time-consuming, thereby requiring automated sentiment classification methods that are accurate and computationally efficient. This study aims to compare the performance of the Multinomial Naïve Bayes and Random Forest algorithms in classifying sentiment in Tokopedia product reviews using the PRDECT-ID dataset, which consists of 5,400 Indonesian-language reviews. The research methodology involved several preprocessing stages, including case folding, cleaning, normalization, tokenization, stopword removal, and stemming using the Sastrawi library, followed by feature extraction using the TF-IDF method. The dataset was divided using a stratified random split approach with 80% training data and 20% testing data, and the models were evaluated using accuracy, precision, recall, F1-score, and ROC-AUC metrics. The results indicate that Multinomial Naïve Bayes outperformed Random Forest, achieving an accuracy of 93.59%, precision of 91.82%, recall of 94.65%, F1-score of 93.21%, and ROC-AUC of 0.9813. In comparison, Random Forest achieved an accuracy of 90.35%, precision of 85.63%, recall of 93.67%, F1-score of 89.47%, and ROC-AUC of 0.9635. In addition to its superior classification performance, Multinomial Naïve Bayes also demonstrated greater computational efficiency with significantly faster training time. These findings suggest that Multinomial Naïve Bayes is a more effective approach for sentiment classification of Indonesian-language e-commerce product reviews.
Sistem Pakar Diagnosis Penyakit Kulit Pada Manusia Dengan Metode Naïve Bayes Menggunakan Shiny Muhammad Hidayatullah; Hanif Rahmat; Retno Mayapada
Jurnal Teknik Mesin, Elektro dan Ilmu Komputer Vol. 3 No. 3 (2023): November : Jurnal Teknik Mesin, Elektro dan Ilmu Komputer
Publisher : Lembaga Pengembangan Kinerja Dosen

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55606/teknik.v3i3.2444

Abstract

Skin is the outermost part of the body's organs which is very susceptible to being influenced by the environment. Besides environmental factors, behavior and lifestyle could also be the cause of skin diseases. An expert system for diagnosing skin diseases can help patients determine the diagnosis of the disease they are suffering from based on the symptoms they feel more efficiently. This system can also provide early detection to patients so that the disease suffered by them can be treated quickly. This research develops a website-based expert system for diagnosing skin diseases in humans using the Naïve Bayes Classifier (NBC) method. The expert system in this research was developed using Shiny Package in R Programming with 10 types of disease and 26 symptoms data. The results of this research show that the expert system has been successful in diagnosing human skin diseases.
Co-Authors Afif Budi Andy B Hanif Rahmat Irwan Usman Kusnaeni Kusnaeni Muh. Rifandi Retno Mayapada